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======= Analysis of Meeting Subject Lines

Introduction

Column

Introduction

In this report, you will find analysis on meeting subject lines. This will allow you to refine meeting exclusion rules and identify topics that drive collaboration patterns.

Column

Word cloud

Word Frequency

Column

Word Frequency - Plot

Column

Word Frequency - Table

Phrase Frequency

Column

Phrase Frequency - Plot

Column

Phrase Frequency - Table

Word co-occurrence

Column

Word co-occurrence - Plot

Column

Word co-occurrence - Table

Top terms

Column

Top terms - Days

Column

Top terms - Hours

---
params:
  data: data
  stopwords: stopwords
  set_title: report_title
  keep: keep
  seed: seed
title: "`r params$set_title`"  
---

```{r echo=FALSE, message=FALSE, warning=FALSE}
library(wpa)

## Get user data
data <- params$data
stopwords <- params$stopwords
keep <- params$keep
seed <- params$seed
```

Introduction
===============================================

Column {data-width=40%}
-------------------------------------

### Introduction

In this report, you will find analysis on **meeting subject lines**. This will allow you to refine meeting exclusion rules and identify topics that drive collaboration patterns.

Column {data-width=60%}
-------------------------------------

### Word cloud

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_wordcloud(stopwords = stopwords, keep = keep)
```

Word Frequency
===============================================

Column {data-width=50%}
-------------------------------------

### Word Frequency - Plot

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_freq(token = "words", stopwords = stopwords, keep = keep)
```

Column {data-width=50%}
-------------------------------------

### Word Frequency - Table

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>%
  tm_freq(
    token = "words",
    stopwords = stopwords,
    keep = keep,
    return = "table"
    ) %>%
  create_dt()
```

Phrase Frequency
===============================================

Column {data-width=50%}
-------------------------------------

### Phrase Frequency - Plot

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_freq(token = "ngrams", stopwords = stopwords, keep = keep)
```

Column {data-width=50%}
-------------------------------------

### Phrase Frequency - Table

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>%
  tm_freq(
    token = "ngrams",
    stopwords = stopwords,
    keep = keep,
    return = "table"
    ) %>%
  create_dt()
```

Word co-occurrence
===============================================

Column {data-width=50%}
-------------------------------------

### Word co-occurrence - Plot

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_cooc(stopwords = stopwords, seed = seed, return = "plot")
```

Column {data-width=50%}
-------------------------------------

### Word co-occurrence - Table
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>%
  tm_cooc(stopwords = stopwords, seed = seed, return = "table") %>%
  create_dt()
```

Top terms
===============================================

Column {data-width=50%}
-------------------------------------

### Top terms - Days

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% subject_scan(mode = "days", stopwords = stopwords)
```

Column {data-width=50%}
-------------------------------------

### Top terms - Hours

```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% subject_scan(mode = "hours", stopwords = stopwords)
```


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